Ultrasonic Sensor Parameter Adaptation for Vehicle Collision Warning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle ultrasonic systems face challenges in maintaining reliable obstacle detection and collision risk assessment due to variations in sensitivity and environmental conditions, leading to degraded sensing performance and prolonged initialization times.
Innovation Solution
An intelligent ultrasonic system that includes a camera sensor unit, ultrasonic signal input unit, feature extraction unit, data collection unit, and control unit to classify surrounding environments and dynamically adjust sensor parameters using machine learning algorithms, optimizing sensing performance and reducing initialization time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If initialization operation is performed to correct sensitivity differences among ultrasonic sensors, then sensing reliability is improved, but initialization time increases considerably
Solution Approach 1:
The system performs preliminary classification of surrounding environments using machine learning algorithms before ultrasonic sensing operations begin. By pre-establishing environment categories (rain, snow, fog, clear weather) and their corresponding optimal parameters, the system eliminates the need for time-consuming initialization operations during actual use, thus reducing initialization time while maintaining sensing reliability.
Solution Approach 2:
The system dynamically changes ultrasonic sensor parameters based on classified environmental conditions. Different parameters (thresholds, gain values, filtering coefficients) are applied for different environments (rain, snow, fog, clear weather), allowing the system to adapt quickly without performing repeated feedback-based initialization operations, thereby resolving the contradiction between reliability and initialization time.
2Reliability
If traditional sensor initialization is used to normalize sensing function, then sensing performance is corrected, but the system cannot adapt to environmental changes (climate, humidity, weather)
Solution Approach 1:
The system transitions from static initialization parameters to dynamic parameter adjustment based on real-time environmental classification. The machine learning model continuously classifies surrounding environments and adjusts ultrasonic sensor parameters accordingly, enabling the system to adapt to changing climate, humidity, and weather conditions while maintaining normalized sensing performance.
Solution Approach 2:
The system implements a feedback mechanism where classification results of surrounding environments are used to adjust sensor parameters. The machine learning algorithm processes environmental data, classifies the current condition, and feeds back optimal parameters to the ultrasonic sensor control unit, creating a closed-loop system that adapts to environmental changes while maintaining sensing reliability.
3Reliability
If multiple feedback processes are repeated during initialization operation, then sensing function normalization is achieved, but system complexity increases
Solution Approach 1:
The system replaces the mechanical feedback-based initialization process with an information-processing approach using machine learning algorithms. Instead of repeating physical feedback cycles to normalize sensing functions, the system uses computational classification of environmental conditions to determine optimal parameters, reducing process complexity while achieving the same normalization goal more efficiently.
4Reliability
If detection reliability is improved through multiple sensing methods, then collision warning accuracy is enhanced, but detection reliability degrades depending on selected method and environment
Solution Approach 1:
The system creates a universal solution that works across multiple environments by classifying surrounding conditions into distinct categories (rain, snow, fog, clear weather) and applying environment-specific parameters. This multi-functional approach allows the ultrasonic sensor to maintain high detection reliability across different environmental conditions, overcoming the limitation of single-method detection that degrades in certain environments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances the stability and efficiency of ultrasonic sensing by adapting to environmental changes, improving detection reliability and reducing the time required for normalizing sensing functions, while also enabling the calculation of collision risk indices and prefill braking pressure for emergency braking.
Implementation Method 1
an ultrasonic signal input unit (110) configured to input an ultrasonic signal sensed through one or more ultrasonic sensors (22)
Data Source
AI summary
An intelligent ultrasonic system may include: a camera sensor unit configured to take an image of a road ahead of a driving vehicle; an ultrasonic signal input unit configured to receive an ultrasonic signal sensed through one or more ultrasonic sensors mounted on the vehicle; a feature extraction unit configured to extract a feature of the received ultrasonic signal; a data collision unit configured to collect one or more data related to a surrounding situation of the road on which the vehicle is driven; and a control unit configured to divide the surrounding situation into two or more classes based on the one or more data collected through the data collection unit, and change or reset an existing parameter to a parameter corresponding to any one class of the classes when the surrounding situation corresponds to the one class or is changed to the one class.